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Record W1965085847 · doi:10.1139/f07-052

Optimal growth temperature hypothesis: Why do anchovy flourish and sardine collapse or vice versa under the same ocean regime?

2007· article· en· W1965085847 on OpenAlexvenueno aff
Akinori Takasuka, Yoshioki Oozeki, Ichiro Aoki

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsSardineAnchovyEngraulisFisheryOtolithBiologyPopulationFishingOceanographyFish <Actinopterygii>Geology

Abstract

fetched live from OpenAlex

The out-of-phase population oscillations between anchovy and sardine have been attributed to climate changes. However, the biological processes causing these species alternations have remained unresolved. Here we propose a simple "optimal growth temperature" hypothesis, in which anchovy and sardine regime shifts are caused by differential optimal temperatures for growth rates during the early life stages. Dome-shaped relationships between growth rate and sea temperature were detected for both Japanese anchovy (Engraulis japonicus) and Japanese sardine (Sardinops melanostictus) larvae based on otolith microstructure analysis. The optimal growth rate for anchovy larvae occurred at 22.0 °C, whereas that for sardine larvae occurred at 16.2 °C. Ambient temperatures have historically fluctuated between these optima, which could lead to contrasting fluctuations in larval growth rates between the two species. This simple mechanism could potentially cause the shifts between the warm anchovy regime and the cool sardine regime in the western North Pacific. Although retrospective analysis suggested synergistic effects of other factors (e.g., trophic interactions and fishing), the optimal growth temperature concept would provide a possible biological mechanism of anchovy and sardine regime shifts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.225
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations232
Published2007
Admission routes1
Has abstractyes

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